We use both real-world data and data generated from simulation for learning domain-invariant 3D shapes.
As shown in Table REF , our real-world dataset contains 187 distinct object instances in total, covering 8 object categories: balls, bottles cans, bowls, cups, mugs, wine glasses, and plates.
To obtain sufficient objects for training our shape prediction model, we use additional CAD models from ShapeNet [1]} and Kitchenware [2]}.
We use an off-the-shelf simulator PyBullet [3]} to create virtual scenes in the simulation.
For ShapeNet, we select \(1,345\)  objects from 6 categories: bags, bottles, bowls, cans, jars, and mugs, which are similar to the objects of our real-world dataset.
Moreover, we use Procedural dataset [4]} containing \(1,000\)  procedurally generated object shapes for evaluation.
